# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py
from __future__ import annotations

from typing import List, Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
import comfy.ldm.common_dit

from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
from comfy.ldm.modules.attention import optimized_attention_masked
from comfy.ldm.flux.layers import EmbedND
from comfy.ldm.flux.math import apply_rope
import comfy.patcher_extension


def modulate(x, scale):
    return x * (1 + scale.unsqueeze(1))

#############################################################################
#                               Core NextDiT Model                              #
#############################################################################

def clamp_fp16(x):
    if x.dtype == torch.float16:
        return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
    return x

class JointAttention(nn.Module):
    """Multi-head attention module."""

    def __init__(
        self,
        dim: int,
        n_heads: int,
        n_kv_heads: Optional[int],
        qk_norm: bool,
        out_bias: bool = False,
        operation_settings={},
    ):
        """
        Initialize the Attention module.

        Args:
            dim (int): Number of input dimensions.
            n_heads (int): Number of heads.
            n_kv_heads (Optional[int]): Number of kv heads, if using GQA.

        """
        super().__init__()
        self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads
        self.n_local_heads = n_heads
        self.n_local_kv_heads = self.n_kv_heads
        self.n_rep = self.n_local_heads // self.n_local_kv_heads
        self.head_dim = dim // n_heads

        self.qkv = operation_settings.get("operations").Linear(
            dim,
            (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,
            bias=False,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )
        self.out = operation_settings.get("operations").Linear(
            n_heads * self.head_dim,
            dim,
            bias=out_bias,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )

        if qk_norm:
            self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
            self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
        else:
            self.q_norm = self.k_norm = nn.Identity()

    def forward(
        self,
        x: torch.Tensor,
        x_mask: torch.Tensor,
        freqs_cis: torch.Tensor,
        transformer_options={},
    ) -> torch.Tensor:
        """

        Args:
            x:
            x_mask:
            freqs_cis:

        Returns:

        """
        bsz, seqlen, _ = x.shape

        xq, xk, xv = torch.split(
            self.qkv(x),
            [
                self.n_local_heads * self.head_dim,
                self.n_local_kv_heads * self.head_dim,
                self.n_local_kv_heads * self.head_dim,
            ],
            dim=-1,
        )
        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)

        xq = self.q_norm(xq)
        xk = self.k_norm(xk)

        xq, xk = apply_rope(xq, xk, freqs_cis)

        n_rep = self.n_local_heads // self.n_local_kv_heads
        if n_rep >= 1:
            xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
            xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options)

        return self.out(output)


class FeedForward(nn.Module):
    def __init__(
        self,
        dim: int,
        hidden_dim: int,
        multiple_of: int,
        ffn_dim_multiplier: Optional[float],
        operation_settings={},
    ):
        """
        Initialize the FeedForward module.

        Args:
            dim (int): Input dimension.
            hidden_dim (int): Hidden dimension of the feedforward layer.
            multiple_of (int): Value to ensure hidden dimension is a multiple
                of this value.
            ffn_dim_multiplier (float, optional): Custom multiplier for hidden
                dimension. Defaults to None.

        """
        super().__init__()
        # custom dim factor multiplier
        if ffn_dim_multiplier is not None:
            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)

        self.w1 = operation_settings.get("operations").Linear(
            dim,
            hidden_dim,
            bias=False,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )
        self.w2 = operation_settings.get("operations").Linear(
            hidden_dim,
            dim,
            bias=False,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )
        self.w3 = operation_settings.get("operations").Linear(
            dim,
            hidden_dim,
            bias=False,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )

    # @torch.compile
    def _forward_silu_gating(self, x1, x3):
        return clamp_fp16(F.silu(x1) * x3)

    def forward(self, x):
        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))


class JointTransformerBlock(nn.Module):
    def __init__(
        self,
        layer_id: int,
        dim: int,
        n_heads: int,
        n_kv_heads: int,
        multiple_of: int,
        ffn_dim_multiplier: float,
        norm_eps: float,
        qk_norm: bool,
        modulation=True,
        z_image_modulation=False,
        attn_out_bias=False,
        operation_settings={},
    ) -> None:
        """
        Initialize a TransformerBlock.

        Args:
            layer_id (int): Identifier for the layer.
            dim (int): Embedding dimension of the input features.
            n_heads (int): Number of attention heads.
            n_kv_heads (Optional[int]): Number of attention heads in key and
                value features (if using GQA), or set to None for the same as
                query.
            multiple_of (int):
            ffn_dim_multiplier (float):
            norm_eps (float):

        """
        super().__init__()
        self.dim = dim
        self.head_dim = dim // n_heads
        self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, out_bias=attn_out_bias, operation_settings=operation_settings)
        self.feed_forward = FeedForward(
            dim=dim,
            hidden_dim=dim,
            multiple_of=multiple_of,
            ffn_dim_multiplier=ffn_dim_multiplier,
            operation_settings=operation_settings,
        )
        self.layer_id = layer_id
        self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
        self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))

        self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
        self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))

        self.modulation = modulation
        if modulation:
            if z_image_modulation:
                self.adaLN_modulation = nn.Sequential(
                    operation_settings.get("operations").Linear(
                        min(dim, 256),
                        4 * dim,
                        bias=True,
                        device=operation_settings.get("device"),
                        dtype=operation_settings.get("dtype"),
                    ),
                )
            else:
                self.adaLN_modulation = nn.Sequential(
                    nn.SiLU(),
                    operation_settings.get("operations").Linear(
                        min(dim, 1024),
                        4 * dim,
                        bias=True,
                        device=operation_settings.get("device"),
                        dtype=operation_settings.get("dtype"),
                    ),
                )

    def forward(
        self,
        x: torch.Tensor,
        x_mask: torch.Tensor,
        freqs_cis: torch.Tensor,
        adaln_input: Optional[torch.Tensor]=None,
        transformer_options={},
    ):
        """
        Perform a forward pass through the TransformerBlock.

        Args:
            x (torch.Tensor): Input tensor.
            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.

        Returns:
            torch.Tensor: Output tensor after applying attention and
                feedforward layers.

        """
        if self.modulation:
            assert adaln_input is not None
            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)

            x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
                clamp_fp16(self.attention(
                    modulate(self.attention_norm1(x), scale_msa),
                    x_mask,
                    freqs_cis,
                    transformer_options=transformer_options,
                ))
            )
            x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
                clamp_fp16(self.feed_forward(
                    modulate(self.ffn_norm1(x), scale_mlp),
                ))
            )
        else:
            assert adaln_input is None
            x = x + self.attention_norm2(
                clamp_fp16(self.attention(
                    self.attention_norm1(x),
                    x_mask,
                    freqs_cis,
                    transformer_options=transformer_options,
                ))
            )
            x = x + self.ffn_norm2(
                self.feed_forward(
                    self.ffn_norm1(x),
                )
            )
        return x


class FinalLayer(nn.Module):
    """
    The final layer of NextDiT.
    """

    def __init__(self, hidden_size, patch_size, out_channels, z_image_modulation=False, operation_settings={}):
        super().__init__()
        self.norm_final = operation_settings.get("operations").LayerNorm(
            hidden_size,
            elementwise_affine=False,
            eps=1e-6,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )
        self.linear = operation_settings.get("operations").Linear(
            hidden_size,
            patch_size * patch_size * out_channels,
            bias=True,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )

        if z_image_modulation:
            min_mod = 256
        else:
            min_mod = 1024

        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            operation_settings.get("operations").Linear(
                min(hidden_size, min_mod),
                hidden_size,
                bias=True,
                device=operation_settings.get("device"),
                dtype=operation_settings.get("dtype"),
            ),
        )

    def forward(self, x, c):
        scale = self.adaLN_modulation(c)
        x = modulate(self.norm_final(x), scale)
        x = self.linear(x)
        return x


class NextDiT(nn.Module):
    """
    Diffusion model with a Transformer backbone.
    """

    def __init__(
        self,
        patch_size: int = 2,
        in_channels: int = 4,
        dim: int = 4096,
        n_layers: int = 32,
        n_refiner_layers: int = 2,
        n_heads: int = 32,
        n_kv_heads: Optional[int] = None,
        multiple_of: int = 256,
        ffn_dim_multiplier: float = 4.0,
        norm_eps: float = 1e-5,
        qk_norm: bool = False,
        cap_feat_dim: int = 5120,
        axes_dims: List[int] = (16, 56, 56),
        axes_lens: List[int] = (1, 512, 512),
        rope_theta=10000.0,
        z_image_modulation=False,
        time_scale=1.0,
        pad_tokens_multiple=None,
        clip_text_dim=None,
        image_model=None,
        device=None,
        dtype=None,
        operations=None,
    ) -> None:
        super().__init__()
        self.dtype = dtype
        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
        self.in_channels = in_channels
        self.out_channels = in_channels
        self.patch_size = patch_size
        self.time_scale = time_scale
        self.pad_tokens_multiple = pad_tokens_multiple

        self.x_embedder = operation_settings.get("operations").Linear(
            in_features=patch_size * patch_size * in_channels,
            out_features=dim,
            bias=True,
            device=operation_settings.get("device"),
            dtype=operation_settings.get("dtype"),
        )

        self.noise_refiner = nn.ModuleList(
            [
                JointTransformerBlock(
                    layer_id,
                    dim,
                    n_heads,
                    n_kv_heads,
                    multiple_of,
                    ffn_dim_multiplier,
                    norm_eps,
                    qk_norm,
                    modulation=True,
                    z_image_modulation=z_image_modulation,
                    operation_settings=operation_settings,
                )
                for layer_id in range(n_refiner_layers)
            ]
        )
        self.context_refiner = nn.ModuleList(
            [
                JointTransformerBlock(
                    layer_id,
                    dim,
                    n_heads,
                    n_kv_heads,
                    multiple_of,
                    ffn_dim_multiplier,
                    norm_eps,
                    qk_norm,
                    modulation=False,
                    operation_settings=operation_settings,
                )
                for layer_id in range(n_refiner_layers)
            ]
        )

        self.t_embedder = TimestepEmbedder(min(dim, 1024), output_size=256 if z_image_modulation else None, **operation_settings)
        self.cap_embedder = nn.Sequential(
            operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
            operation_settings.get("operations").Linear(
                cap_feat_dim,
                dim,
                bias=True,
                device=operation_settings.get("device"),
                dtype=operation_settings.get("dtype"),
            ),
        )

        self.clip_text_pooled_proj = None

        if clip_text_dim is not None:
            self.clip_text_dim = clip_text_dim
            self.clip_text_pooled_proj = nn.Sequential(
                operation_settings.get("operations").RMSNorm(clip_text_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
                operation_settings.get("operations").Linear(
                    clip_text_dim,
                    clip_text_dim,
                    bias=True,
                    device=operation_settings.get("device"),
                    dtype=operation_settings.get("dtype"),
                ),
            )
            self.time_text_embed = nn.Sequential(
                nn.SiLU(),
                operation_settings.get("operations").Linear(
                    min(dim, 1024) + clip_text_dim,
                    min(dim, 1024),
                    bias=True,
                    device=operation_settings.get("device"),
                    dtype=operation_settings.get("dtype"),
                ),
            )

        self.layers = nn.ModuleList(
            [
                JointTransformerBlock(
                    layer_id,
                    dim,
                    n_heads,
                    n_kv_heads,
                    multiple_of,
                    ffn_dim_multiplier,
                    norm_eps,
                    qk_norm,
                    z_image_modulation=z_image_modulation,
                    attn_out_bias=False,
                    operation_settings=operation_settings,
                )
                for layer_id in range(n_layers)
            ]
        )
        self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
        self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings)

        if self.pad_tokens_multiple is not None:
            self.x_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))
            self.cap_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))

        assert (dim // n_heads) == sum(axes_dims)
        self.axes_dims = axes_dims
        self.axes_lens = axes_lens
        self.rope_embedder = EmbedND(dim=dim // n_heads, theta=rope_theta, axes_dim=axes_dims)
        self.dim = dim
        self.n_heads = n_heads

    def unpatchify(
        self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False
    ) -> List[torch.Tensor]:
        """
        x: (N, T, patch_size**2 * C)
        imgs: (N, H, W, C)
        """
        pH = pW = self.patch_size
        imgs = []
        for i in range(x.size(0)):
            H, W = img_size[i]
            begin = cap_size[i]
            end = begin + (H // pH) * (W // pW)
            imgs.append(
                x[i][begin:end]
                .view(H // pH, W // pW, pH, pW, self.out_channels)
                .permute(4, 0, 2, 1, 3)
                .flatten(3, 4)
                .flatten(1, 2)
            )

        if return_tensor:
            imgs = torch.stack(imgs, dim=0)
        return imgs

    def patchify_and_embed(
        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={}
    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
        bsz = len(x)
        pH = pW = self.patch_size
        device = x[0].device

        if self.pad_tokens_multiple is not None:
            pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple
            cap_feats = torch.cat((cap_feats, self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype, copy=True).unsqueeze(0).repeat(cap_feats.shape[0], pad_extra, 1)), dim=1)

        cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device)
        cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0

        B, C, H, W = x.shape
        x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))

        rope_options = transformer_options.get("rope_options", None)
        h_scale = 1.0
        w_scale = 1.0
        h_start = 0
        w_start = 0
        if rope_options is not None:
            h_scale = rope_options.get("scale_y", 1.0)
            w_scale = rope_options.get("scale_x", 1.0)

            h_start = rope_options.get("shift_y", 0.0)
            w_start = rope_options.get("shift_x", 0.0)

        H_tokens, W_tokens = H // pH, W // pW
        x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device)
        x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1
        x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
        x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()

        if self.pad_tokens_multiple is not None:
            pad_extra = (-x.shape[1]) % self.pad_tokens_multiple
            x = torch.cat((x, self.x_pad_token.to(device=x.device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(x.shape[0], pad_extra, 1)), dim=1)
            x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra))

        freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2)

        # refine context
        for layer in self.context_refiner:
            cap_feats = layer(cap_feats, cap_mask, freqs_cis[:, :cap_pos_ids.shape[1]], transformer_options=transformer_options)

        padded_img_mask = None
        for layer in self.noise_refiner:
            x = layer(x, padded_img_mask, freqs_cis[:, cap_pos_ids.shape[1]:], t, transformer_options=transformer_options)

        padded_full_embed = torch.cat((cap_feats, x), dim=1)
        mask = None
        img_sizes = [(H, W)] * bsz
        l_effective_cap_len = [cap_feats.shape[1]] * bsz
        return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis

    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
            self._forward,
            self,
            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
        ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)

    # def forward(self, x, t, cap_feats, cap_mask):
    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
        t = 1.0 - timesteps
        cap_feats = context
        cap_mask = attention_mask
        bs, c, h, w = x.shape
        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
        """
        Forward pass of NextDiT.
        t: (N,) tensor of diffusion timesteps
        y: (N,) tensor of text tokens/features
        """

        t = self.t_embedder(t * self.time_scale, dtype=x.dtype)  # (N, D)
        adaln_input = t

        cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute

        if self.clip_text_pooled_proj is not None:
            pooled = kwargs.get("clip_text_pooled", None)
            if pooled is not None:
                pooled = self.clip_text_pooled_proj(pooled)
            else:
                pooled = torch.zeros((1, self.clip_text_dim), device=x.device, dtype=x.dtype)

            adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))

        patches = transformer_options.get("patches", {})
        x_is_tensor = isinstance(x, torch.Tensor)
        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
        freqs_cis = freqs_cis.to(img.device)

        for i, layer in enumerate(self.layers):
            img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
            if "double_block" in patches:
                for p in patches["double_block"]:
                    out = p({"img": img[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
                    if "img" in out:
                        img[:, cap_size[0]:] = out["img"]
                    if "txt" in out:
                        img[:, :cap_size[0]] = out["txt"]

        img = self.final_layer(img, adaln_input)
        img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]

        return -img

